> For the complete documentation index, see [llms.txt](https://redi-school-1.gitbook.io/teacher-applicant-hub/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://redi-school-1.gitbook.io/teacher-applicant-hub/data-ai-track/machine-learning-ai-hamburg.md).
# Machine Learning / AI (Hamburg)
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### **๐จ This Course Is Now Full**
Please note that there are no places left for this course. We encourage you to take a look at the other courses where your support may be needed.
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### ๐ Course Details
* **Schedule:** Monday & Wednesday, 19:00โ21:00 (Online)
* 1 Class in person during the semester.
* **Format:** Live online lectures with practical exercises.
* **Final Outcome:** Mini Project & Final project.
***
### ๐ฅ Volunteer Roles
* **Session Owner:** Leads classes and introduces new concepts.
* **Session Assistant:** Supports students and manages Zoom logistics during class.
* **Backup Teacher:** Steps in when regular teachers are unavailable.
***
### ๐งฉ Topics Covered
* Introduction & EDA
* Welcome & Workspace Prep + Coding Advice
* Data Cleaning + EDA
* EDA, Pandas, NumPy + Matplotlib
* Machine Learning Intro
* ML Intro โ Theory: What are Models, High-Level Overview, Feature Engineering
* Regression
* ML Regression โ Theory: Univariate, Polynomial, Multivariate Regression
* ML Regression + Regularization โ Theory: Under/Overfitting, Train/Validation/Test Split, Resampling Techniques
* Model Preparation & Evaluation
* ML Model Prep โ Theory: Model Evaluation, Hyperparameter Tuning, Overfitting & Underfitting
* Classification
* ML Classification โ Theory: Confusion Matrix, ROC, Classification Concepts
* ML Classification โ Practical: Logistic Regression, Random Forest, etc.
* Clustering
* ML Clustering โ Theory: K-means, Hierarchical, Density-Based Clustering
* NLP & LLMs
* NLP โ Theory
* LLMs โ Theory
* Transformers LLMs โ RAG & Theory
***
### ๐ Final Project
At the end of the course, students complete a **final project** and give a **presentation** demonstrating their learning.
The project consists of applying a strong foundation, covering supervised learning methods like regression and classification, as well as unsupervised techniques such as clustering and dimensionality reduction.
Take a look at **an example** of what learners will have achieved by the end. ๐
[Hotel Cancellation Predictor](https://drive.google.com/drive/u/4/folders/1LRd4d14t7Li7RTwZGQhf7ZofljG8tcjf)
***
Thank you for supporting the ReDI students ๐งก
### More info:
* Find out more [About ReDI](/teacher-applicant-hub/about/about-redi-school.md)
* Find out more [About DCP](/teacher-applicant-hub/about/dcp-online.md)
* Find out more [About this Course](https://www.redi-school.org/data-analytics/hamburg/dcp/machine-learning)
* Find out about our [How to Volunteer](/teacher-applicant-hub/about/your-volunteer-journey.md)
### FAQ
* Check out: [I can't teach tonight](https://app.gitbook.com/o/-MX7viJUQOkaASHKwdfd/s/uejRUS3xfsruSGvRjFWJ/data-ai-track/python-foundations-hamburg), [I am dropping out](https://app.gitbook.com/o/-MX7viJUQOkaASHKwdfd/s/uejRUS3xfsruSGvRjFWJ/data-ai-track/python-foundations-hamburg), [I feel uncomfortable](https://app.gitbook.com/o/-MX7viJUQOkaASHKwdfd/s/uejRUS3xfsruSGvRjFWJ/data-ai-track/python-foundations-hamburg).
* In case you have questions, write an email to .